A case study in multi-core parallelism for the reliability evaluation of composite power systems

The probabilistic evaluation of composite power system reliability is an important but computationally intense task that requires the sampling/searching of a large search space. While multiple methods have been used for performing these computations, a remaining area of research is the impact that modern platforms for parallel computation may have on this computation. Studies have been performed in the past, but they have been primarily limited to cluster-based computing. In addition, the most recent works in this area have used outdated technology or been evaluated using smaller test systems. In the modern era, a wide variety of platforms are available for achieving parallelism in computation including options like multi-core processors, clusters, and accelerators. Each of these platforms provides unique opportunities for accelerating computation and exploiting scalability. In order to fill this gap in the research, this study implements and evaluates two methods of parallel computation-batch parallelism and pipeline parallelism-using a multi-core architecture in a cloud computing environment on Amazon Web Services using up to 36 virtual compute cores. Further, the methodologies are contrasted and compared in terms of computation time, speedup, efficiency, and scalability. Results are collected using IEEE reliability test systems, and speedups upwards of 5x are demonstrated across multiple test systems.